SPARQL Generation with Entity Pre-trained GPT for KG Question Answering
This work addresses enabling non-programmer users to query knowledge graphs, but it is incremental as it builds on existing entity linking and GPT-based methods.
The paper tackles the problem of generating SPARQL queries from natural language questions for knowledge graph question answering, achieving 62.703% accuracy on exact SPARQL matches in a 3-shot setting.
Knowledge Graphs popularity has been rapidly growing in last years. All that knowledge is available for people to query it through the many online databases on the internet. Though, it would be a great achievement if non-programmer users could access whatever information they want to know. There has been a lot of effort oriented to solve this task using natural language processing tools and creativity encouragement by way of many challenges. Our approach focuses on assuming a correct entity linking on the natural language questions and training a GPT model to create SPARQL queries from them. We managed to isolate which property of the task can be the most difficult to solve at few or zero-shot and we proposed pre-training on all entities (under CWA) to improve the performance. We obtained a 62.703% accuracy of exact SPARQL matches on testing at 3-shots, a F1 of 0.809 on the entity linking challenge and a F1 of 0.009 on the question answering challenge.